@inproceedings{SauerZindlerGorksetal.2021, author = {Sauer, Timm and Zindler, Klaus and Gorks, Manuel and Spielmann, Luca and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using self-learning controllers considering a priori plant knowledge}, series = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, booktitle = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, year = {2021}, abstract = {This paper presents a new self-learning control scheme for lateral track guidance of industrial trucks using artificial intelligence. It is an universally applicable lateral dynamic control concept which is able to adapt itself to different truck variants. Moreover it shall consider vehicle parameter variations that occur during operation, such as the load dependent change of vehicle mass and moment of inertia. The proposed approach uses Reinforcement Learning. In order to reduce the training effort, a new concept is realized, taking into account a priori knowledge of vehicle behavior. Its fundamental idea consists of dividing the training process into two steps. In the first step the controller will be pre-trained on basis of a nominal model representing a priori knowledge of lateral dynamic vehicle behavior. Since this model is derived for an industrial truck with average vehicle parameter values, a fine tuning of the control parameters has to be performed in the second step. In this way the controller is adapted to the actual truck variant and the corresponding vehicle parameter values. In order to demonstrate the efficiency of the proposed control scheme, the simulation results given in this paper are compared to the closed loop behavior using standard LQR.}, subject = {Flurf{\"o}rderer}, language = {en} } @inproceedings{SauerGorksSpielmannetal.2022, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks with time-variant vehicle parameters using AI-based controllers}, series = {Advanes in Automotive Control - AAC}, volume = {2022}, booktitle = {Advanes in Automotive Control - AAC}, pages = {7}, year = {2022}, abstract = {This paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only finetuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a RL control concept, not considering time-variant parameters.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SauerSpielmannGorksetal.2022, author = {Sauer, Timm and Spielmann, Luca and Gorks, Manuel and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using AI-based controllers with disturbance compensation}, series = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, booktitle = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, year = {2022}, abstract = {This paper presents a new control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significanlty improves the control quality. In order to demonstrate this, the new approach is compared to a RL control concept, not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{SauerGorksSpielmannetal.2022, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus}, title = {Automatische Spurf{\"u}hrung von Flurf{\"o}rderzeugen mittels KI}, series = {ATZ heavyduty}, volume = {15}, journal = {ATZ heavyduty}, number = {04/2022}, pages = {44 -- 47}, year = {2022}, abstract = {Im Verbundprojekt KAnIS forscht die TH Aschaffenburg mit Linde Material Handling an neuen KI-basierten Algorithmen zur querdynamischen Fahrzeugf{\"u}hrung von Flurf{\"o}rderzeugen. Ziel des Projekts ist die Entwicklung eines universell einsetzbaren Regelungskonzepts, das verschiedene Arten von Flurf{\"o}rderzeugen automatisch f{\"u}hrt, im Betrieb auftretenden Fahrzeugparameterschwankungen gezielt Rechnung tr{\"a}gt und dar{\"u}ber hinaus auch den jeweiligen Fitnesszustand der Fahrzeuge ber{\"u}cksichtigt.}, subject = {Flurf{\"o}rderer}, language = {de} } @article{SauerGorksSpielmannetal.2023, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus and Jumar, Ulrich}, title = {Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks}, series = {SICE Journal of Control, Measurement and System Integration}, volume = {2023}, journal = {SICE Journal of Control, Measurement and System Integration}, number = {Vol. 16, No. 1}, doi = {10.1080/18824889.2023.2183009}, pages = {84 -- 97}, year = {2023}, abstract = {This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift's dynamic is approximated within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared to an RL control concept, which is not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.}, subject = {Flurf{\"o}rderer}, language = {en} }